Proceedings of the Annual Meeting of the Cognitive Science Society

The Availability bias, manifested in the over-representation ofextreme eventualities, is a well-known cognitive bias, and isgenerally taken as evidence of human irrationality. In thiswork, we present the first rational, metacognitive account ofthe Availability bias, formally articulated at Marr’s algorith-mic level of analysis. Concretely, we present a normative,metacognitive model of how a cognitive system should over-represent extreme eventualities, depending on the amount oftime available for decision-making. Our Sample-based Ex-pected Utility model also accounts for two well-known fram-ing effects in human decision-making under risk—the fourfoldpattern of risk preferences in outcome probability (Tversky& Kahneman, 1992) and in outcome magnitude (Markovitz,1952)—thereby providing the first metacognitively-rationalbasis for the aforementioned effects. Empirical evidence con-firms an important prediction of our model. Surprisingly, ourmodel is strikingly robust with respect to its focal parameter.We discuss the implications of our work for studies on hu-man decision-making, and conclude by presenting a counter-intuitive prediction of our model, which, if confirmed, wouldhave intriguing implications for human decision-making un-der risk. To our knowledge, our model is the first metacog-nitive, resource-rational process model of cognitive biases indecision-making. Notably, our work also contributes to thefields of artificial intelligence and computational statistics, bypresenting a previously unknown proposal distribution, withfirm rational grounds, broadly applicable to the influential sub-field of importance sampling Monte Carlo methods.